b2h5py 0.2.0

Transparent optimized reading of n-dimensional Blosc2 slices for h5py

b2h5py provides h5py with transparent, automatic optimized reading of n-dimensional slices of Blosc2-compressed datasets. This optimized slicing leverages direct chunk access (skipping the slow HDF5 filter pipeline) and 2-level partitioning into chunks and then smaller blocks (so that less data is actually decompressed).

Benchmarks of this technique show 2x-5x speed-ups compared with normal filter-based access. Comparable results are obtained with a similar technique in PyTables, see Optimized Hyper-slicing in PyTables with Blosc2 NDim.

Usage

This optimized access works for slices with step 1 on Blosc2-compressed datasets using the native byte order. It is enabled by monkey-patching the h5py.Dataset class to extend the slicing operation. This is done on module import, so the only thing you need to do is:

import b2h5py

After that, optimization will be attempted for any slicing of a dataset (of the form dataset[...] or dataset.__getitem__(...)). If the optimization is not possible in a particular case, normal h5py slicing code will be used (which performs HDF5 filter-based access, backed by hdf5plugin to support Blosc2).

You may globally disable the optimization after importing b2h5py by calling b2h5py.unpatch_dataset_class(), and enable it again with b2h5py.patch_dataset_class(). You may also perform this patching temporarily using patching_dataset_class() to get a context manager.

Even if the module is imported and the Dataset class is patched, you may still force-disable the optimization by setting BLOSC2_FILTER=1 in the environment.

Building

Just install PyPA build (e.g. pip install build), enter the source code directory and run pyproject-build to get a source tarball and a wheel under the dist directory.

Installing

To install as a wheel from PyPI, run pip install b2h5py.

You may also install the wheel that you built in the previous section, or enter the source code directory and run pip install . from there.

Running tests

If you have installed b2h5py, just run python -m unittest discover b2h5py.tests.

Otherwise, just enter its source code directory and run python -m unittest.

You can also run the h5py tests with the patched Dataset class to check that patching does not break anything. You may install the h5py-test extra (e.g. pip install b2h5py[h5py-test] and run python -m b2h5py.tests.test_patched_h5py.